Population variation in genetic programming
Information Sciences: an International Journal
Genetic programming for cross-task knowledge sharing
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Knowledge reuse in genetic programming applied to visual learning
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Generative learning of visual concepts using multiobjective genetic programming
Pattern Recognition Letters
Classifier design with feature selection and feature extraction using layered genetic programming
Expert Systems with Applications: An International Journal
Multitask visual learning using genetic programming
Evolutionary Computation
Learning and Recognition of Hand-Drawn Shapes Using Generative Genetic Programming
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Evolving novel image features using genetic programming-based image transforms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Just in time classifiers: managing the slow drift case
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Linear dimensionality reduction using relevance weighted LDA
Pattern Recognition
Automatic induction of projection pursuit indices
IEEE Transactions on Neural Networks
On supporting identification in a hand-based biometric framework
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Learning high-level visual concepts using attributed primitives and genetic programming
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Genetic graph programming for object detection
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Artificial Intelligence Review
| Hi-index | 0.00 |
A hybrid evolutionary learning algorithm is presented that synthesizes a complete multiclass pattern recognition system. The approach uses a multifaceted representation that evolves layers of processing to perform feature extraction from raw input data, select cooperative sets of feature detectors, and assemble a linear classifier that uses the detectors' responses to label targets. The hybrid algorithm, called hybrid evolutionary learning for pattern recognition (HELPR), blends elements of evolutionary programming, genetic programming, and genetic algorithms to perform a search for an effective set of feature detectors. Individual detectors are represented as expressions composed of morphological and arithmetic operations. Starting with a few small random expressions, HELPR expands the number and complexity of the features to produce a recognition system that achieves high accuracy. Results are presented that demonstrate the performance of HELPR-generated recognition systems applied to the task of classification of high-range resolution radar signals.